Soft Sensor design for a Topping process in the case of small datasets

In this paper, a new strategy to cope with the identification of nonlinear models of industrial processes, when a limited number of experimental data is available, is proposed. The approach is intended to improve the generalization capabilities of the model and it is based on the integration of bootstrap resampling, noise injection and neural model stacking. A number of algorithms to stack the first level neural models are also compared. The method proposed has been applied to develop a Soft Sensor for the estimation of the Freezing Point of Kerosene in an atmospheric distillation unit (Topping) working in a refinery in Sicily, Italy. The improvements obtained thanks to the strategy proposed, with respect to a classical neural model, are shown in the paper.